This is a benchmarking single-cell data comprising of 3 different human lung adenocarcinoma cell lines. This experiment was designed induce different batch effects into the data.

Integration challenge

  • Prior to integration, there is a strong separation effect by multiple batch effects.
  • One of the batch effects is the different protocols with significant depth differences.
  • The experimental design poses extra difficulties as cell lines were cultured separatedly and the same batch was processed in 3 different ways.

Data description

  • Data source:
Type of merge Name ID Author DOI or URL Protocol Organism Tissue # of cell types # of cells # of batches
Across platforms with significant depth difference CellBench CellBench Cel-seq2, Drop-seq, 10x Chromium Human Adenocarcinoma cell lines 3 1401 3 per cell types
  • Relation to the scMerge article: Supplementary Figure 12.

Data visualisation

tSNE plots by cell types and batch

Integrated scMerge data

  • Data availability: CellBench Data (in RData format)

  • scMerge parameters for integration:

    • Unsupervised scMerge
    • kmeans K = (3,3,3)
    • Negative controls are human scSEG